1. Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network

    Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network

    Segmentation of retinal layers in optical coherence tomography (OCT) is an essential step in OCT image analysis for screening, diagnosis, and assessment of retinal disease progression. Real-time segmentation together with high-speed OCT volume acquisition allows rendering of en face OCT of arbitrary retinal layers, which can be used to increase the yield rate of high-quality scans, provide real-time feedback during image-guided surgeries, and compensate aberrations in adaptive optics (AO) OCT without using wavefront sensors. We demonstrate here unprecedented real-time OCT segmentation of eight retinal layer boundaries achieved by 3 levels of optimization: 1) a modified, low complexity, neural network structure ...

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